Overview

Dataset statistics

Number of variables46
Number of observations538624
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.0 MiB
Average record size in memory239.4 B

Variable types

Numeric13
Categorical32
DateTime1

Alerts

BoolBridle has constant value "0"Constant
Material_Flexible Polyvinyl Chloride has constant value "0"Constant
Town has a high cardinality: 1548 distinct valuesHigh cardinality
Risk_S*I/Inspections is highly overall correlated with leakage_estimate_factor and 4 other fieldsHigh correlation
leakage_estimate_factor is highly overall correlated with Risk_S*I/Inspections and 2 other fieldsHigh correlation
Risk_S*I is highly overall correlated with Risk_S*I/Inspections and 3 other fieldsHigh correlation
Length is highly overall correlated with NumConnectionsHigh correlation
Pressure is highly overall correlated with Material_Acrylonitrile-Butadiene-Styrene and 1 other fieldsHigh correlation
NumConnections is highly overall correlated with LengthHigh correlation
TownCount is highly overall correlated with Province and 1 other fieldsHigh correlation
No_Incidents is highly overall correlated with Risk_S*I/Inspections and 2 other fieldsHigh correlation
Severity is highly overall correlated with Risk_S*I/Inspections and 1 other fieldsHigh correlation
Incidence is highly overall correlated with Risk_S*I/Inspections and 4 other fieldsHigh correlation
Province is highly overall correlated with TownCount and 9 other fieldsHigh correlation
Autonomía_Andalucía is highly overall correlated with ProvinceHigh correlation
Autonomía_Aragón is highly overall correlated with ProvinceHigh correlation
Autonomía_Castilla y León is highly overall correlated with ProvinceHigh correlation
Autonomía_Castilla-La Mancha is highly overall correlated with ProvinceHigh correlation
Autonomía_Cataluña is highly overall correlated with ProvinceHigh correlation
Autonomía_Comunidad Valenciana is highly overall correlated with ProvinceHigh correlation
Autonomía_Galicia is highly overall correlated with ProvinceHigh correlation
Autonomía_Madrid (Comunidad de) is highly overall correlated with TownCount and 1 other fieldsHigh correlation
Autonomía_Rioja (La) is highly overall correlated with ProvinceHigh correlation
Material_Acrylonitrile-Butadiene-Styrene is highly overall correlated with Pressure and 1 other fieldsHigh correlation
Material_Polyethylene is highly overall correlated with Pressure and 1 other fieldsHigh correlation
No_Incidents is highly imbalanced (97.0%)Imbalance
Severity is highly imbalanced (98.6%)Imbalance
Incidence is highly imbalanced (97.5%)Imbalance
NumConnectionsUnder is highly imbalanced (99.8%)Imbalance
gas_natural is highly imbalanced (78.1%)Imbalance
Autonomía_Andalucía is highly imbalanced (53.5%)Imbalance
Autonomía_Aragón is highly imbalanced (97.6%)Imbalance
Autonomía_Balears (Illes) is highly imbalanced (97.6%)Imbalance
Autonomía_Castilla y León is highly imbalanced (53.1%)Imbalance
Autonomía_Castilla-La Mancha is highly imbalanced (68.7%)Imbalance
Autonomía_Extremadura is highly imbalanced (99.2%)Imbalance
Autonomía_Galicia is highly imbalanced (59.9%)Imbalance
Autonomía_Madrid (Comunidad de) is highly imbalanced (61.9%)Imbalance
Autonomía_Navarra (Comunidad Foral de) is highly imbalanced (99.6%)Imbalance
Autonomía_Rioja (La) is highly imbalanced (90.3%)Imbalance
Material_Acrylonitrile-Butadiene-Styrene is highly imbalanced (52.4%)Imbalance
Material_Copper is highly imbalanced (94.7%)Imbalance
Material_Fiberglass is highly imbalanced (> 99.9%)Imbalance
Material_Fiberglass-Reinforced Plastic is highly imbalanced (82.6%)Imbalance
Material_Flexible Polyolefin is highly imbalanced (> 99.9%)Imbalance
Material_Polyamide is highly imbalanced (> 99.9%)Imbalance
Material_Polypropylene is highly imbalanced (91.3%)Imbalance
Material_Polyvinylidene Fluoride is highly imbalanced (> 99.9%)Imbalance
Material_Zinc-Coated Steel is highly imbalanced (98.6%)Imbalance
Length is highly skewed (γ1 = 108.8647578)Skewed
Risk_S*I/Inspections has 534280 (99.2%) zerosZeros
leakage_estimate_factor has 534327 (99.2%) zerosZeros
Risk_S*I has 534280 (99.2%) zerosZeros
NumConnections has 355408 (66.0%) zerosZeros

Reproduction

Analysis started2023-02-13 01:51:19.147263
Analysis finished2023-02-13 01:53:04.370154
Duration1 minute and 45.22 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

PipeId
Real number (ℝ)

Distinct536806
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9159698 × 108
Minimum489616
Maximum4.5119541 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:04.450298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum489616
5-th percentile8953335.2
Q156553179
median1.8992081 × 108
Q32.9710674 × 108
95-th percentile4.062379 × 108
Maximum4.5119541 × 108
Range4.5070579 × 108
Interquartile range (IQR)2.4055356 × 108

Descriptive statistics

Standard deviation1.2235306 × 108
Coefficient of variation (CV)0.63859598
Kurtosis-0.96361961
Mean1.9159698 × 108
Median Absolute Deviation (MAD)1.0988686 × 108
Skewness0.067395222
Sum1.0319873 × 1014
Variance1.4970272 × 1016
MonotonicityNot monotonic
2023-02-13T02:53:04.565959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189590185 2
 
< 0.1%
26715614 2
 
< 0.1%
48454355 2
 
< 0.1%
48454220 2
 
< 0.1%
2225237 2
 
< 0.1%
189633271 2
 
< 0.1%
189633278 2
 
< 0.1%
189590664 2
 
< 0.1%
189633243 2
 
< 0.1%
189702067 2
 
< 0.1%
Other values (536796) 538604
> 99.9%
ValueCountFrequency (%)
489616 1
< 0.1%
489645 1
< 0.1%
489646 1
< 0.1%
490306 1
< 0.1%
490308 1
< 0.1%
490310 1
< 0.1%
490347 1
< 0.1%
490349 1
< 0.1%
490351 1
< 0.1%
490355 1
< 0.1%
ValueCountFrequency (%)
451195406 1
< 0.1%
451195364 1
< 0.1%
451195194 1
< 0.1%
451195177 1
< 0.1%
451194745 1
< 0.1%
451194115 1
< 0.1%
451194098 1
< 0.1%
451193984 1
< 0.1%
451193967 1
< 0.1%
451181923 1
< 0.1%

Inspections
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4097292
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:04.669101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q35
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4986212
Coefficient of variation (CV)0.33984427
Kurtosis-0.30269728
Mean4.4097292
Median Absolute Deviation (MAD)1
Skewness-0.83153858
Sum2375186
Variance2.2458656
MonotonicityNot monotonic
2023-02-13T02:53:04.745204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 215064
39.9%
6 126841
23.5%
3 56720
 
10.5%
2 53680
 
10.0%
4 52828
 
9.8%
1 30832
 
5.7%
7 2429
 
0.5%
10 93
 
< 0.1%
8 76
 
< 0.1%
11 33
 
< 0.1%
ValueCountFrequency (%)
1 30832
 
5.7%
2 53680
 
10.0%
3 56720
 
10.5%
4 52828
 
9.8%
5 215064
39.9%
6 126841
23.5%
7 2429
 
0.5%
8 76
 
< 0.1%
9 28
 
< 0.1%
10 93
 
< 0.1%
ValueCountFrequency (%)
11 33
 
< 0.1%
10 93
 
< 0.1%
9 28
 
< 0.1%
8 76
 
< 0.1%
7 2429
 
0.5%
6 126841
23.5%
5 215064
39.9%
4 52828
 
9.8%
3 56720
 
10.5%
2 53680
 
10.0%

No_Incidents
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
534280 
1
 
4052
2
 
273
3
 
18
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 534280
99.2%
1 4052
 
0.8%
2 273
 
0.1%
3 18
 
< 0.1%
5 1
 
< 0.1%

Length

2023-02-13T02:53:04.827534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:04.926128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 534280
99.2%
1 4052
 
0.8%
2 273
 
0.1%
3 18
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 534280
99.2%
1 4052
 
0.8%
2 273
 
0.1%
3 18
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 534280
99.2%
1 4052
 
0.8%
2 273
 
0.1%
3 18
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 534280
99.2%
1 4052
 
0.8%
2 273
 
0.1%
3 18
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 534280
99.2%
1 4052
 
0.8%
2 273
 
0.1%
3 18
 
< 0.1%
5 1
 
< 0.1%

Risk_S*I/Inspections
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010056002
Minimum0
Maximum3
Zeros534280
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:05.018000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.13004314
Coefficient of variation (CV)12.931894
Kurtosis312.38262
Mean0.010056002
Median Absolute Deviation (MAD)0
Skewness16.449343
Sum5416.4038
Variance0.016911219
MonotonicityNot monotonic
2023-02-13T02:53:05.124293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 534280
99.2%
0.76 732
 
0.1%
0.6388888889 701
 
0.1%
1.75 592
 
0.1%
1.222222222 542
 
0.1%
3 469
 
0.1%
0.72 238
 
< 0.1%
0.9375 182
 
< 0.1%
0.6111111111 166
 
< 0.1%
0.68 112
 
< 0.1%
Other values (28) 610
 
0.1%
ValueCountFrequency (%)
0 534280
99.2%
0.37 1
 
< 0.1%
0.4074074074 1
 
< 0.1%
0.5306122449 2
 
< 0.1%
0.5510204082 21
 
< 0.1%
0.5833333333 101
 
< 0.1%
0.6111111111 166
 
< 0.1%
0.6388888889 701
 
0.1%
0.68 112
 
< 0.1%
0.72 238
 
< 0.1%
ValueCountFrequency (%)
3 469
0.1%
2.5 5
 
< 0.1%
2.4375 5
 
< 0.1%
2.222222222 52
 
< 0.1%
2.04 2
 
< 0.1%
2 99
 
< 0.1%
1.8 1
 
< 0.1%
1.777777778 3
 
< 0.1%
1.75 592
0.1%
1.625 4
 
< 0.1%

leakage_estimate_factor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct216
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1118042
Minimum0
Maximum84
Zeros534327
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:05.244997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum84
Range84
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4870354
Coefficient of variation (CV)13.300353
Kurtosis448.57466
Mean0.1118042
Median Absolute Deviation (MAD)0
Skewness18.53478
Sum60220.427
Variance2.2112742
MonotonicityNot monotonic
2023-02-13T02:53:05.361316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 534327
99.2%
9.12 436
 
0.1%
21 297
 
0.1%
7.027777778 294
 
0.1%
36 199
 
< 0.1%
7.666666667 183
 
< 0.1%
8.64 178
 
< 0.1%
9.5 159
 
< 0.1%
14.66666667 158
 
< 0.1%
7.347222222 109
 
< 0.1%
Other values (206) 2284
 
0.4%
ValueCountFrequency (%)
0 534327
99.2%
0.5 1
 
< 0.1%
1 1
 
< 0.1%
1.5 7
 
< 0.1%
2 1
 
< 0.1%
2.25 1
 
< 0.1%
2.625 1
 
< 0.1%
3 5
 
< 0.1%
3.5 1
 
< 0.1%
3.666666667 1
 
< 0.1%
ValueCountFrequency (%)
84 2
< 0.1%
77 1
 
< 0.1%
72 2
< 0.1%
71.25 1
 
< 0.1%
64.75 2
< 0.1%
64.5 1
 
< 0.1%
63.875 1
 
< 0.1%
63 4
< 0.1%
60 1
 
< 0.1%
58.5 1
 
< 0.1%

InspectionDay
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
Tuesday
112077 
Wednesday
110227 
Monday
106165 
Thursday
101448 
Friday
85577 
Other values (2)
23130 

Length

Max length9
Median length8
Mean length7.257185
Min length6

Characters and Unicode

Total characters3908894
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Tuesday 112077
20.8%
Wednesday 110227
20.5%
Monday 106165
19.7%
Thursday 101448
18.8%
Friday 85577
15.9%
Saturday 15748
 
2.9%
Sunday 7382
 
1.4%

Length

2023-02-13T02:53:05.464286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:05.575963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 112077
20.8%
wednesday 110227
20.5%
monday 106165
19.7%
thursday 101448
18.8%
friday 85577
15.9%
saturday 15748
 
2.9%
sunday 7382
 
1.4%

Most occurring characters

ValueCountFrequency (%)
d 648851
16.6%
a 554372
14.2%
y 538624
13.8%
e 332531
8.5%
s 323752
8.3%
u 236655
 
6.1%
n 223774
 
5.7%
T 213525
 
5.5%
r 202773
 
5.2%
W 110227
 
2.8%
Other values (7) 523810
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3370270
86.2%
Uppercase Letter 538624
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 648851
19.3%
a 554372
16.4%
y 538624
16.0%
e 332531
9.9%
s 323752
9.6%
u 236655
 
7.0%
n 223774
 
6.6%
r 202773
 
6.0%
o 106165
 
3.2%
h 101448
 
3.0%
Other values (2) 101325
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
T 213525
39.6%
W 110227
20.5%
M 106165
19.7%
F 85577
15.9%
S 23130
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3908894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 648851
16.6%
a 554372
14.2%
y 538624
13.8%
e 332531
8.5%
s 323752
8.3%
u 236655
 
6.1%
n 223774
 
5.7%
T 213525
 
5.5%
r 202773
 
5.2%
W 110227
 
2.8%
Other values (7) 523810
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3908894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 648851
16.6%
a 554372
14.2%
y 538624
13.8%
e 332531
8.5%
s 323752
8.3%
u 236655
 
6.1%
n 223774
 
5.7%
T 213525
 
5.5%
r 202773
 
5.2%
W 110227
 
2.8%
Other values (7) 523810
13.4%

InspectionYear
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4662
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:05.683166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2016
Q12020
median2020
Q32020
95-th percentile2020
Maximum2021
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3966583
Coefficient of variation (CV)0.00069159775
Kurtosis11.300656
Mean2019.4662
Median Absolute Deviation (MAD)0
Skewness-3.2743138
Sum1.0877329 × 109
Variance1.9506543
MonotonicityNot monotonic
2023-02-13T02:53:05.762646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2020 429528
79.7%
2019 40210
 
7.5%
2018 27275
 
5.1%
2017 11758
 
2.2%
2016 8982
 
1.7%
2014 5638
 
1.0%
2015 5545
 
1.0%
2013 5366
 
1.0%
2012 2033
 
0.4%
2021 1445
 
0.3%
Other values (2) 844
 
0.2%
ValueCountFrequency (%)
2010 46
 
< 0.1%
2011 798
 
0.1%
2012 2033
 
0.4%
2013 5366
 
1.0%
2014 5638
 
1.0%
2015 5545
 
1.0%
2016 8982
 
1.7%
2017 11758
 
2.2%
2018 27275
5.1%
2019 40210
7.5%
ValueCountFrequency (%)
2021 1445
 
0.3%
2020 429528
79.7%
2019 40210
 
7.5%
2018 27275
 
5.1%
2017 11758
 
2.2%
2016 8982
 
1.7%
2015 5545
 
1.0%
2014 5638
 
1.0%
2013 5366
 
1.0%
2012 2033
 
0.4%
Distinct2944
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
Minimum2010-10-01 00:00:00
Maximum2020-12-31 00:00:00
2023-02-13T02:53:05.872871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:53:05.989340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MonthsLastRev
Real number (ℝ)

Distinct116
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.557099
Minimum0
Maximum121
Zeros705
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:06.110012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q124
median24
Q324
95-th percentile48
Maximum121
Range121
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.8095055
Coefficient of variation (CV)0.30557089
Kurtosis24.23241
Mean25.557099
Median Absolute Deviation (MAD)0
Skewness4.2310541
Sum13765667
Variance60.988376
MonotonicityNot monotonic
2023-02-13T02:53:06.228248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 323355
60.0%
25 63351
 
11.8%
23 54903
 
10.2%
22 27368
 
5.1%
48 19823
 
3.7%
21 9050
 
1.7%
26 8606
 
1.6%
49 3491
 
0.6%
47 3232
 
0.6%
19 2186
 
0.4%
Other values (106) 23259
 
4.3%
ValueCountFrequency (%)
0 705
0.1%
1 15
 
< 0.1%
2 29
 
< 0.1%
3 161
 
< 0.1%
4 107
 
< 0.1%
5 248
 
< 0.1%
6 195
 
< 0.1%
7 152
 
< 0.1%
8 180
 
< 0.1%
9 437
0.1%
ValueCountFrequency (%)
121 4
< 0.1%
120 3
 
< 0.1%
119 3
 
< 0.1%
118 2
 
< 0.1%
116 2
 
< 0.1%
115 1
 
< 0.1%
114 2
 
< 0.1%
113 1
 
< 0.1%
111 8
< 0.1%
109 2
 
< 0.1%

Risk_S*I
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030207975
Minimum0
Maximum15
Zeros534280
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:06.340629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3468928
Coefficient of variation (CV)11.483484
Kurtosis179.48474
Mean0.030207975
Median Absolute Deviation (MAD)0
Skewness12.567627
Sum16270.74
Variance0.12033462
MonotonicityNot monotonic
2023-02-13T02:53:06.445019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 534280
99.2%
3.8 732
 
0.1%
3.5 701
 
0.1%
3.833333333 701
 
0.1%
3.666666667 646
 
0.1%
3 484
 
0.1%
3.6 238
 
< 0.1%
3.75 182
 
< 0.1%
3.4 112
 
< 0.1%
2 98
 
< 0.1%
Other values (27) 450
 
0.1%
ValueCountFrequency (%)
0 534280
99.2%
1 56
 
< 0.1%
2 98
 
< 0.1%
2.5 18
 
< 0.1%
3 484
 
0.1%
3.25 22
 
< 0.1%
3.333333333 40
 
< 0.1%
3.4 112
 
< 0.1%
3.5 701
 
0.1%
3.6 238
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
10.5 3
 
< 0.1%
10.2 2
 
< 0.1%
9.75 5
 
< 0.1%
9 7
 
< 0.1%
7.8 1
 
< 0.1%
7.428571429 1
 
< 0.1%
7.333333333 63
< 0.1%
7.2 33
< 0.1%
7 33
< 0.1%

Severity
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
4
537298 
3
 
988
2
 
239
1
 
99

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 537298
99.8%
3 988
 
0.2%
2 239
 
< 0.1%
1 99
 
< 0.1%

Length

2023-02-13T02:53:06.548505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:06.639876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 537298
99.8%
3 988
 
0.2%
2 239
 
< 0.1%
1 99
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 537298
99.8%
3 988
 
0.2%
2 239
 
< 0.1%
1 99
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 537298
99.8%
3 988
 
0.2%
2 239
 
< 0.1%
1 99
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 537298
99.8%
3 988
 
0.2%
2 239
 
< 0.1%
1 99
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 537298
99.8%
3 988
 
0.2%
2 239
 
< 0.1%
1 99
 
< 0.1%

Incidence
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
537298 
1
 
1326

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 537298
99.8%
1 1326
 
0.2%

Length

2023-02-13T02:53:06.719074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:06.807014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 537298
99.8%
1 1326
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 537298
99.8%
1 1326
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 537298
99.8%
1 1326
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 537298
99.8%
1 1326
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 537298
99.8%
1 1326
 
0.2%

YearBuilt
Real number (ℝ)

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.6969
Minimum1901
Maximum2050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:06.895547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile1986
Q11999
median2004
Q32009
95-th percentile2016
Maximum2050
Range149
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.971783
Coefficient of variation (CV)0.0059778309
Kurtosis23.422184
Mean2002.6969
Median Absolute Deviation (MAD)5
Skewness-3.5221777
Sum1.0787006 × 109
Variance143.3236
MonotonicityNot monotonic
2023-02-13T02:53:07.011903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002 30483
 
5.7%
2004 28834
 
5.4%
2003 27399
 
5.1%
2008 26291
 
4.9%
2005 25599
 
4.8%
2001 24696
 
4.6%
2006 24198
 
4.5%
2009 24024
 
4.5%
2007 23769
 
4.4%
2016 21601
 
4.0%
Other values (76) 281730
52.3%
ValueCountFrequency (%)
1901 2299
0.4%
1920 1
 
< 0.1%
1925 1
 
< 0.1%
1927 2
 
< 0.1%
1928 1
 
< 0.1%
1929 3
 
< 0.1%
1930 7
 
< 0.1%
1936 2
 
< 0.1%
1938 1
 
< 0.1%
1940 1
 
< 0.1%
ValueCountFrequency (%)
2050 4
 
< 0.1%
2022 15
 
< 0.1%
2021 78
 
< 0.1%
2020 1174
 
0.2%
2019 3254
 
0.6%
2018 4665
 
0.9%
2017 6671
 
1.2%
2016 21601
4.0%
2015 18003
3.3%
2014 13988
2.6%

Diameter
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.39431
Minimum10
Maximum609.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:07.134222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile50.8
Q163
median110
Q3160
95-th percentile203.2
Maximum609.6
Range599.6
Interquartile range (IQR)97

Descriptive statistics

Standard deviation58.249309
Coefficient of variation (CV)0.50044808
Kurtosis4.5138646
Mean116.39431
Median Absolute Deviation (MAD)47
Skewness1.5273333
Sum62692768
Variance3392.9819
MonotonicityNot monotonic
2023-02-13T02:53:07.245895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 129572
24.1%
63 102376
19.0%
90 96497
17.9%
160 65108
12.1%
200 47302
 
8.8%
40 16795
 
3.1%
152.4 13077
 
2.4%
101.6 9911
 
1.8%
203.2 9408
 
1.7%
50.8 6566
 
1.2%
Other values (50) 42012
 
7.8%
ValueCountFrequency (%)
10 17
 
< 0.1%
11 22
 
< 0.1%
12 301
 
0.1%
12.7 2
 
< 0.1%
13 13
 
< 0.1%
14 20
 
< 0.1%
15 1139
0.2%
16 144
 
< 0.1%
18 2
 
< 0.1%
19 794
0.1%
ValueCountFrequency (%)
609.6 112
 
< 0.1%
558.8 11
 
< 0.1%
508 330
 
0.1%
500 2
 
< 0.1%
457.2 24
 
< 0.1%
406.4 1606
0.3%
400 30
 
< 0.1%
355.6 261
 
< 0.1%
355 3
 
< 0.1%
350 16
 
< 0.1%

Length
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct131598
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.274663
Minimum0
Maximum26100.943
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:07.367095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q12.004
median10.504
Q340.50025
95-th percentile134.3094
Maximum26100.943
Range26100.943
Interquartile range (IQR)38.49625

Descriptive statistics

Standard deviation86.10019
Coefficient of variation (CV)2.5120653
Kurtosis31237.541
Mean34.274663
Median Absolute Deviation (MAD)9.63
Skewness108.86476
Sum18461156
Variance7413.2428
MonotonicityNot monotonic
2023-02-13T02:53:07.493144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6647
 
1.2%
0.5 6246
 
1.2%
1.002 2502
 
0.5%
2 2440
 
0.5%
1.001 1972
 
0.4%
0.3 1568
 
0.3%
0.501 1507
 
0.3%
1.003 1445
 
0.3%
0.8 1439
 
0.3%
0.4 1389
 
0.3%
Other values (131588) 511469
95.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.005 9
 
< 0.1%
0.006 9
 
< 0.1%
0.007 7
 
< 0.1%
0.008 15
< 0.1%
0.009 14
< 0.1%
0.01 30
< 0.1%
0.011 13
< 0.1%
0.012 9
 
< 0.1%
0.013 15
< 0.1%
ValueCountFrequency (%)
26100.943 1
< 0.1%
26030.149 1
< 0.1%
7291.366 1
< 0.1%
4738.89 1
< 0.1%
4690.917 1
< 0.1%
4322.733 1
< 0.1%
4308.87 1
< 0.1%
3776.983 1
< 0.1%
3706.524 1
< 0.1%
3504.288 1
< 0.1%

Pressure
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6150839
Minimum0.025
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:07.618880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.025
5-th percentile0.025
Q10.15
median0.15
Q34
95-th percentile16
Maximum80
Range79.975
Interquartile range (IQR)3.85

Descriptive statistics

Standard deviation8.3253149
Coefficient of variation (CV)2.3029382
Kurtosis28.484064
Mean3.6150839
Median Absolute Deviation (MAD)0.125
Skewness4.9334775
Sum1947171
Variance69.310868
MonotonicityNot monotonic
2023-02-13T02:53:07.711376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 161618
30.0%
0.15 143140
26.6%
0.025 75069
13.9%
0.1 51769
 
9.6%
16 31790
 
5.9%
0.4 20779
 
3.9%
1.7 20408
 
3.8%
5 16372
 
3.0%
49.5 8276
 
1.5%
0.05 2693
 
0.5%
Other values (10) 6710
 
1.2%
ValueCountFrequency (%)
0.025 75069
13.9%
0.05 2693
 
0.5%
0.1 51769
 
9.6%
0.15 143140
26.6%
0.4 20779
 
3.9%
1.7 20408
 
3.8%
2 1508
 
0.3%
4 161618
30.0%
5 16372
 
3.0%
10 1006
 
0.2%
ValueCountFrequency (%)
80 648
 
0.1%
72 340
 
0.1%
59.5 1057
 
0.2%
49.5 8276
 
1.5%
45 1232
 
0.2%
40 234
 
< 0.1%
36 135
 
< 0.1%
25 25
 
< 0.1%
16 31790
5.9%
12 525
 
0.1%

NumConnections
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84908025
Minimum0
Maximum83
Zeros355408
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:07.829782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum83
Range83
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0248212
Coefficient of variation (CV)2.384723
Kurtosis63.635289
Mean0.84908025
Median Absolute Deviation (MAD)0
Skewness5.8037013
Sum457335
Variance4.0999008
MonotonicityNot monotonic
2023-02-13T02:53:07.942183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 355408
66.0%
1 93773
 
17.4%
2 37165
 
6.9%
3 17871
 
3.3%
4 10898
 
2.0%
5 6558
 
1.2%
6 4505
 
0.8%
7 3021
 
0.6%
8 2238
 
0.4%
9 1608
 
0.3%
Other values (44) 5579
 
1.0%
ValueCountFrequency (%)
0 355408
66.0%
1 93773
 
17.4%
2 37165
 
6.9%
3 17871
 
3.3%
4 10898
 
2.0%
5 6558
 
1.2%
6 4505
 
0.8%
7 3021
 
0.6%
8 2238
 
0.4%
9 1608
 
0.3%
ValueCountFrequency (%)
83 1
 
< 0.1%
65 1
 
< 0.1%
63 1
 
< 0.1%
58 4
< 0.1%
55 1
 
< 0.1%
54 1
 
< 0.1%
52 2
< 0.1%
50 1
 
< 0.1%
49 2
< 0.1%
48 1
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538440 
1
 
172
2
 
8
3
 
3
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538440
> 99.9%
1 172
 
< 0.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%

Length

2023-02-13T02:53:08.039182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:08.135212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538440
> 99.9%
1 172
 
< 0.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 538440
> 99.9%
1 172
 
< 0.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538440
> 99.9%
1 172
 
< 0.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538440
> 99.9%
1 172
 
< 0.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538440
> 99.9%
1 172
 
< 0.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%

BoolBridle
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538624
100.0%

Length

2023-02-13T02:53:08.445429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:08.530788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538624
100.0%

Most occurring characters

ValueCountFrequency (%)
0 538624
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538624
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538624
100.0%

gas_natural
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
1
519766 
0
 
18858

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 519766
96.5%
0 18858
 
3.5%

Length

2023-02-13T02:53:08.599072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:08.685826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 519766
96.5%
0 18858
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 519766
96.5%
0 18858
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 519766
96.5%
0 18858
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 519766
96.5%
0 18858
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 519766
96.5%
0 18858
 
3.5%

Province
Categorical

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
barcelona
135071 
valencia
52091 
madrid
40315 
girona
36831 
tarragona
32633 
Other values (32)
241683 

Length

Max length11
Median length10
Mean length7.8293652
Min length4

Characters and Unicode

Total characters4217084
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvalencia
2nd rowbarcelona
3rd rowvalencia
4th rowvalencia
5th rowbarcelona

Common Values

ValueCountFrequency (%)
barcelona 135071
25.1%
valencia 52091
 
9.7%
madrid 40315
 
7.5%
girona 36831
 
6.8%
tarragona 32633
 
6.1%
alicante 24149
 
4.5%
pontevedra 18992
 
3.5%
la coruna 18269
 
3.4%
sevilla 17342
 
3.2%
lleida 15756
 
2.9%
Other values (27) 147175
27.3%

Length

2023-02-13T02:53:08.764044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
barcelona 135071
23.7%
valencia 52091
 
9.1%
madrid 40315
 
7.1%
girona 36831
 
6.5%
tarragona 32633
 
5.7%
la 25032
 
4.4%
alicante 24149
 
4.2%
pontevedra 18992
 
3.3%
coruna 18269
 
3.2%
sevilla 17342
 
3.0%
Other values (29) 169708
29.8%

Most occurring characters

ValueCountFrequency (%)
a 914975
21.7%
l 444097
10.5%
r 370090
8.8%
n 363252
 
8.6%
o 350718
 
8.3%
e 350162
 
8.3%
c 273905
 
6.5%
i 225389
 
5.3%
d 193053
 
4.6%
b 155362
 
3.7%
Other values (11) 576081
13.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4185275
99.2%
Space Separator 31809
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 914975
21.9%
l 444097
10.6%
r 370090
8.8%
n 363252
 
8.7%
o 350718
 
8.4%
e 350162
 
8.4%
c 273905
 
6.5%
i 225389
 
5.4%
d 193053
 
4.6%
b 155362
 
3.7%
Other values (10) 544272
13.0%
Space Separator
ValueCountFrequency (%)
31809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4185275
99.2%
Common 31809
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 914975
21.9%
l 444097
10.6%
r 370090
8.8%
n 363252
 
8.7%
o 350718
 
8.4%
e 350162
 
8.4%
c 273905
 
6.5%
i 225389
 
5.4%
d 193053
 
4.6%
b 155362
 
3.7%
Other values (10) 544272
13.0%
Common
ValueCountFrequency (%)
31809
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4217084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 914975
21.7%
l 444097
10.5%
r 370090
8.8%
n 363252
 
8.6%
o 350718
 
8.3%
e 350162
 
8.3%
c 273905
 
6.5%
i 225389
 
5.3%
d 193053
 
4.6%
b 155362
 
3.7%
Other values (11) 576081
13.7%

Town
Categorical

Distinct1548
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
madrid
 
25889
barcelona
 
20125
sant cugat del valles
 
10328
alicante/alacant
 
9099
valencia
 
8372
Other values (1543)
464811 

Length

Max length25
Median length21
Mean length10.601343
Min length3

Characters and Unicode

Total characters5710138
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)< 0.1%

Sample

1st rowbetera
2nd rowsabadell
3rd rowbetera
4th rowbetera
5th rowsabadell

Common Values

ValueCountFrequency (%)
madrid 25889
 
4.8%
barcelona 20125
 
3.7%
sant cugat del valles 10328
 
1.9%
alicante/alacant 9099
 
1.7%
valencia 8372
 
1.6%
malaga 7908
 
1.5%
valladolid 6801
 
1.3%
mataro 6178
 
1.1%
coruna 5759
 
1.1%
burgos 5372
 
1.0%
Other values (1538) 432793
80.4%

Length

2023-02-13T02:53:08.867973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 87643
 
10.1%
del 32810
 
3.8%
madrid 25889
 
3.0%
sant 23056
 
2.7%
la 21729
 
2.5%
barcelona 20125
 
2.3%
valles 18861
 
2.2%
cugat 10529
 
1.2%
llobregat 9366
 
1.1%
alicante/alacant 9099
 
1.0%
Other values (1721) 610685
70.2%

Most occurring characters

ValueCountFrequency (%)
a 966554
16.9%
l 557296
9.8%
e 548801
9.6%
r 435081
 
7.6%
o 351349
 
6.2%
331168
 
5.8%
n 308794
 
5.4%
d 308635
 
5.4%
i 282059
 
4.9%
s 271169
 
4.7%
Other values (18) 1349232
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5351609
93.7%
Space Separator 331168
 
5.8%
Other Punctuation 20011
 
0.4%
Dash Punctuation 7350
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 966554
18.1%
l 557296
10.4%
e 548801
10.3%
r 435081
8.1%
o 351349
 
6.6%
n 308794
 
5.8%
d 308635
 
5.8%
i 282059
 
5.3%
s 271169
 
5.1%
t 239508
 
4.5%
Other values (14) 1082363
20.2%
Other Punctuation
ValueCountFrequency (%)
/ 19014
95.0%
. 997
 
5.0%
Space Separator
ValueCountFrequency (%)
331168
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5351609
93.7%
Common 358529
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 966554
18.1%
l 557296
10.4%
e 548801
10.3%
r 435081
8.1%
o 351349
 
6.6%
n 308794
 
5.8%
d 308635
 
5.8%
i 282059
 
5.3%
s 271169
 
5.1%
t 239508
 
4.5%
Other values (14) 1082363
20.2%
Common
ValueCountFrequency (%)
331168
92.4%
/ 19014
 
5.3%
- 7350
 
2.1%
. 997
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5710138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 966554
16.9%
l 557296
9.8%
e 548801
9.6%
r 435081
 
7.6%
o 351349
 
6.2%
331168
 
5.8%
n 308794
 
5.4%
d 308635
 
5.4%
i 282059
 
4.9%
s 271169
 
4.7%
Other values (18) 1349232
23.6%

TownCount
Real number (ℝ)

Distinct891
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9619.236
Minimum1
Maximum75616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-02-13T02:53:08.976108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile257
Q11122
median2267
Q37754
95-th percentile58408
Maximum75616
Range75615
Interquartile range (IQR)6632

Descriptive statistics

Standard deviation18694.769
Coefficient of variation (CV)1.9434775
Kurtosis6.2332696
Mean9619.236
Median Absolute Deviation (MAD)1655
Skewness2.7365682
Sum5.1811514 × 109
Variance3.4949439 × 108
MonotonicityNot monotonic
2023-02-13T02:53:09.093921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75616 25889
 
4.8%
58408 20125
 
3.7%
10864 10328
 
1.9%
13899 9099
 
1.7%
25405 8372
 
1.6%
16363 7908
 
1.5%
13369 6801
 
1.3%
7869 6178
 
1.1%
9570 5759
 
1.1%
8602 5372
 
1.0%
Other values (881) 432793
80.4%
ValueCountFrequency (%)
1 17
 
< 0.1%
2 24
 
< 0.1%
3 32
 
< 0.1%
4 36
< 0.1%
5 70
< 0.1%
6 54
< 0.1%
7 87
< 0.1%
8 50
< 0.1%
9 75
< 0.1%
10 51
< 0.1%
ValueCountFrequency (%)
75616 25889
4.8%
58408 20125
3.7%
25405 8372
 
1.6%
22491 4253
 
0.8%
16573 551
 
0.1%
16363 7908
 
1.5%
15887 2109
 
0.4%
14213 3786
 
0.7%
13899 9099
 
1.7%
13369 6801
 
1.3%

Autonomía_Andalucía
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
485422 
1
53202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 485422
90.1%
1 53202
 
9.9%

Length

2023-02-13T02:53:09.211361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:09.302428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 485422
90.1%
1 53202
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 485422
90.1%
1 53202
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 485422
90.1%
1 53202
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 485422
90.1%
1 53202
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 485422
90.1%
1 53202
 
9.9%

Autonomía_Aragón
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
537344 
1
 
1280

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 537344
99.8%
1 1280
 
0.2%

Length

2023-02-13T02:53:09.378152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:09.467979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 537344
99.8%
1 1280
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 537344
99.8%
1 1280
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 537344
99.8%
1 1280
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 537344
99.8%
1 1280
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 537344
99.8%
1 1280
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
537370 
1
 
1254

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 537370
99.8%
1 1254
 
0.2%

Length

2023-02-13T02:53:09.541982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:09.631469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 537370
99.8%
1 1254
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 537370
99.8%
1 1254
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 537370
99.8%
1 1254
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 537370
99.8%
1 1254
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 537370
99.8%
1 1254
 
0.2%

Autonomía_Castilla y León
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
484790 
1
53834 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 484790
90.0%
1 53834
 
10.0%

Length

2023-02-13T02:53:09.702291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:09.789358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 484790
90.0%
1 53834
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 484790
90.0%
1 53834
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484790
90.0%
1 53834
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484790
90.0%
1 53834
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484790
90.0%
1 53834
 
10.0%

Autonomía_Castilla-La Mancha
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
508229 
1
 
30395

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 508229
94.4%
1 30395
 
5.6%

Length

2023-02-13T02:53:09.861440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:09.947121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 508229
94.4%
1 30395
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 508229
94.4%
1 30395
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 508229
94.4%
1 30395
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 508229
94.4%
1 30395
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 508229
94.4%
1 30395
 
5.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
317033 
1
221591 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 317033
58.9%
1 221591
41.1%

Length

2023-02-13T02:53:10.017979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:10.103724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 317033
58.9%
1 221591
41.1%

Most occurring characters

ValueCountFrequency (%)
0 317033
58.9%
1 221591
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 317033
58.9%
1 221591
41.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 317033
58.9%
1 221591
41.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 317033
58.9%
1 221591
41.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
451758 
1
86866 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 451758
83.9%
1 86866
 
16.1%

Length

2023-02-13T02:53:10.175433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:10.264062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 451758
83.9%
1 86866
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 451758
83.9%
1 86866
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 451758
83.9%
1 86866
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 451758
83.9%
1 86866
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 451758
83.9%
1 86866
 
16.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538266 
1
 
358

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538266
99.9%
1 358
 
0.1%

Length

2023-02-13T02:53:10.340930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:10.428231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538266
99.9%
1 358
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 538266
99.9%
1 358
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538266
99.9%
1 358
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538266
99.9%
1 358
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538266
99.9%
1 358
 
0.1%

Autonomía_Galicia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
495647 
1
 
42977

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 495647
92.0%
1 42977
 
8.0%

Length

2023-02-13T02:53:10.501534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:10.589556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 495647
92.0%
1 42977
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 495647
92.0%
1 42977
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 495647
92.0%
1 42977
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 495647
92.0%
1 42977
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 495647
92.0%
1 42977
 
8.0%

Autonomía_Madrid (Comunidad de)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
498667 
1
 
39957

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 498667
92.6%
1 39957
 
7.4%

Length

2023-02-13T02:53:10.659910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:10.749277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 498667
92.6%
1 39957
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 498667
92.6%
1 39957
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 498667
92.6%
1 39957
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 498667
92.6%
1 39957
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 498667
92.6%
1 39957
 
7.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538477 
1
 
147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538477
> 99.9%
1 147
 
< 0.1%

Length

2023-02-13T02:53:10.861101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:10.953032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538477
> 99.9%
1 147
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 538477
> 99.9%
1 147
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538477
> 99.9%
1 147
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538477
> 99.9%
1 147
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538477
> 99.9%
1 147
 
< 0.1%

Autonomía_Rioja (La)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
531861 
1
 
6763

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 531861
98.7%
1 6763
 
1.3%

Length

2023-02-13T02:53:11.023580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:11.110340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 531861
98.7%
1 6763
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 531861
98.7%
1 6763
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 531861
98.7%
1 6763
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 531861
98.7%
1 6763
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 531861
98.7%
1 6763
 
1.3%

Material_Acrylonitrile-Butadiene-Styrene
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
483548 
1
55076 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 483548
89.8%
1 55076
 
10.2%

Length

2023-02-13T02:53:11.180780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:11.268891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 483548
89.8%
1 55076
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 483548
89.8%
1 55076
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 483548
89.8%
1 55076
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 483548
89.8%
1 55076
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 483548
89.8%
1 55076
 
10.2%

Material_Copper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
535366 
1
 
3258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 535366
99.4%
1 3258
 
0.6%

Length

2023-02-13T02:53:11.344265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:11.431574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 535366
99.4%
1 3258
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 535366
99.4%
1 3258
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 535366
99.4%
1 3258
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 535366
99.4%
1 3258
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 535366
99.4%
1 3258
 
0.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538615 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538615
> 99.9%
1 9
 
< 0.1%

Length

2023-02-13T02:53:11.503494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:11.590899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538615
> 99.9%
1 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 538615
> 99.9%
1 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538615
> 99.9%
1 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538615
> 99.9%
1 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538615
> 99.9%
1 9
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
524606 
1
 
14018

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 524606
97.4%
1 14018
 
2.6%

Length

2023-02-13T02:53:11.661712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:11.747083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 524606
97.4%
1 14018
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 524606
97.4%
1 14018
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 524606
97.4%
1 14018
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 524606
97.4%
1 14018
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 524606
97.4%
1 14018
 
2.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538620 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538620
> 99.9%
1 4
 
< 0.1%

Length

2023-02-13T02:53:11.816028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:11.900524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538620
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 538620
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538620
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538620
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538620
> 99.9%
1 4
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538624
100.0%

Length

2023-02-13T02:53:11.970546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:12.057021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538624
100.0%

Most occurring characters

ValueCountFrequency (%)
0 538624
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538624
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538624
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538611 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538611
> 99.9%
1 13
 
< 0.1%

Length

2023-02-13T02:53:12.124119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:12.208353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538611
> 99.9%
1 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 538611
> 99.9%
1 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538611
> 99.9%
1 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538611
> 99.9%
1 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538611
> 99.9%
1 13
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
1
459662 
0
78962 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 459662
85.3%
0 78962
 
14.7%

Length

2023-02-13T02:53:12.280087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:12.368836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 459662
85.3%
0 78962
 
14.7%

Most occurring characters

ValueCountFrequency (%)
1 459662
85.3%
0 78962
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 459662
85.3%
0 78962
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 459662
85.3%
0 78962
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 459662
85.3%
0 78962
 
14.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
532755 
1
 
5869

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 532755
98.9%
1 5869
 
1.1%

Length

2023-02-13T02:53:12.443999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:12.531774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 532755
98.9%
1 5869
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 532755
98.9%
1 5869
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 532755
98.9%
1 5869
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 532755
98.9%
1 5869
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 532755
98.9%
1 5869
 
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
538612 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538612
> 99.9%
1 12
 
< 0.1%

Length

2023-02-13T02:53:12.602872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:12.688544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 538612
> 99.9%
1 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 538612
> 99.9%
1 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538612
> 99.9%
1 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 538612
> 99.9%
1 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 538612
> 99.9%
1 12
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
537921 
1
 
703

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters538624
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 537921
99.9%
1 703
 
0.1%

Length

2023-02-13T02:53:12.759018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T02:53:12.846129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 537921
99.9%
1 703
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 537921
99.9%
1 703
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 538624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 537921
99.9%
1 703
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 538624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 537921
99.9%
1 703
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 537921
99.9%
1 703
 
0.1%

Interactions

2023-02-13T02:52:58.207094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:35.055315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.856159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:38.717296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:40.634173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:42.669121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:44.964038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:46.939932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:48.774046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:51.060754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.031839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:54.767064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:56.541983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:58.343758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:35.190507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.984134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:38.849180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:40.761411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:42.831895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:45.128256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.065224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:48.982014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:51.213759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.157906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:54.895297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:56.670868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:58.470963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:35.320832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:37.105868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:39.090272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:40.887479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:42.994124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:45.293841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.190159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:49.148067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:51.531475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.286036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.027424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:56.791056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:58.788129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:35.459492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:37.246234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:39.230874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:41.024227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:43.158261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:45.457235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.331472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:49.323000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:51.670068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.423223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.167069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:56.915970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:58.932696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:35.600931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:37.383221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:39.367506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:41.150152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:43.313307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:45.616021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.463014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:49.501145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:51.797896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.544141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.296964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.043570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:59.070494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:35.740580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:37.528084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:39.505511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:41.283229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:43.476208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:45.779624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.593183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:49.678949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:51.930363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.684040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.435019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.170162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:59.202870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:35.877159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:37.683105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:39.645194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:41.431284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:43.638540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:45.940063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.726309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:49.852260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:52.066181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.846228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.572231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.297124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:59.345967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.023586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:37.835675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:39.790193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:41.582034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:43.806224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:46.103040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.858046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:50.030958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:52.203264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:53.979452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.719067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.429770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:59.485836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.165215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:38.021010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:39.939568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:41.777784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:43.983372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:46.261041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:47.996183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:50.204885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:52.340354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:54.113099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.862078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.561156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:59.618053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.301217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:38.168112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:40.078865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:41.953754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:44.147171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:46.405827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:48.131305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:50.372107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:52.483022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:54.243479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:55.999706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.691100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:59.747654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.438930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:38.303064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:40.215116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:42.119680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:44.304175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:46.532205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:48.266065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:50.541014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:52.624030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:54.370076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:56.129041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.812948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:59.890785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.583515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:38.448149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:40.358840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:42.333847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:44.480900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:46.677181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:48.429879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:50.719188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:52.766106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:54.509179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:56.268200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:57.948496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:53:00.019176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:36.717307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:38.579124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:40.490241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:42.484072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:44.785991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:46.803294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:48.590319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:50.882874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:52.892162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:54.629766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:56.399153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T02:52:58.070455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-13T02:53:12.962873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
PipeIdInspectionsRisk_S*I/Inspectionsleakage_estimate_factorInspectionYearMonthsLastRevRisk_S*IYearBuiltDiameterLengthPressureNumConnectionsTownCountNo_IncidentsInspectionDaySeverityIncidenceNumConnectionsUndergas_naturalProvinceAutonomía_AndalucíaAutonomía_AragónAutonomía_Balears (Illes)Autonomía_Castilla y LeónAutonomía_Castilla-La ManchaAutonomía_CataluñaAutonomía_Comunidad ValencianaAutonomía_ExtremaduraAutonomía_GaliciaAutonomía_Madrid (Comunidad de)Autonomía_Navarra (Comunidad Foral de)Autonomía_Rioja (La)Material_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_FiberglassMaterial_Fiberglass-Reinforced PlasticMaterial_Flexible PolyolefinMaterial_PolyamideMaterial_PolyethyleneMaterial_PolypropyleneMaterial_Polyvinylidene FluorideMaterial_Zinc-Coated Steel
PipeId1.0000.309-0.041-0.040-0.0530.112-0.041-0.0080.166-0.0290.001-0.0690.0450.0270.0380.0230.0350.0120.3270.2970.2450.0300.0400.4510.2440.4900.1780.0430.3100.2720.0320.1180.1190.1320.0050.1580.0040.0060.1240.3010.0020.061
Inspections0.3091.000-0.021-0.0200.491-0.124-0.021-0.4390.1270.248-0.1670.1470.2490.0200.0310.0350.0600.0080.3560.1650.1170.0820.0670.1390.1350.1910.1040.0350.1310.2640.0340.0600.1380.1210.0060.0720.0050.0110.1640.2230.0100.078
Risk_S*I/Inspections-0.041-0.0211.0000.9940.001-0.0521.000-0.012-0.0330.061-0.0200.0920.0210.6130.0050.6160.7480.0000.1550.0390.0240.0050.0020.0190.0130.0280.0190.0750.0200.0960.0000.0070.0090.1040.0820.0590.0740.1820.0460.0250.1340.050
leakage_estimate_factor-0.040-0.0200.9941.0000.003-0.0500.995-0.012-0.0330.061-0.0200.0930.0210.4490.0020.4110.6270.0000.1250.0240.0180.0080.0160.0130.0110.0140.0140.0490.0140.0530.0030.0050.0130.0810.0560.0350.1230.1200.0390.0170.1600.045
InspectionYear-0.0530.4910.0010.0031.000-0.0870.001-0.067-0.0200.414-0.0970.2390.0630.0190.0320.0460.0750.0000.0470.1290.1250.0380.0170.1190.0570.1520.0800.0790.0460.0770.0340.0300.1470.0400.0480.0320.0240.0170.1130.0330.0110.014
MonthsLastRev0.112-0.124-0.052-0.050-0.0871.000-0.052-0.0730.075-0.0470.050-0.070-0.0800.0150.0200.0290.0480.0000.0870.1350.0970.0880.0440.0900.1030.0670.0880.0150.0680.1230.0340.0450.3680.0420.0040.0250.0000.0000.3060.2000.0000.013
Risk_S*I-0.041-0.0211.0000.9950.001-0.0521.000-0.013-0.0330.061-0.0200.0920.0210.9970.0020.4150.7020.0000.0930.0370.0050.0030.0000.0180.0090.0100.0180.0410.0160.0890.0000.0070.0070.0710.0680.0450.0610.1360.0430.0240.1300.024
YearBuilt-0.008-0.439-0.012-0.012-0.067-0.073-0.0131.000-0.155-0.0380.224-0.047-0.3940.0480.0210.0800.1320.0040.2140.1550.1370.0260.0260.0950.1050.2540.1560.0290.0990.0720.0040.0430.1090.0840.0350.4270.0140.0420.2560.1310.0260.037
Diameter0.1660.127-0.033-0.033-0.0200.075-0.033-0.1551.0000.035-0.222-0.2070.1580.0230.0240.0200.0310.0030.2850.1800.0900.2040.0320.1650.0680.0820.1650.0350.0290.1540.0110.0690.3540.1340.0040.2260.0030.0020.3710.0550.0030.046
Length-0.0290.2480.0610.0610.414-0.0470.061-0.0380.0351.0000.0590.515-0.0450.0000.0020.0000.0000.0000.0000.0140.0090.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0080.0000.0000.000
Pressure0.001-0.167-0.020-0.020-0.0970.050-0.0200.224-0.2220.0591.000-0.135-0.2440.0090.0430.0050.0100.0000.0580.1830.0450.1470.0140.0710.1380.1150.0470.0070.1380.0590.0360.0520.8760.0230.0000.0490.0000.0000.7130.0310.0000.010
NumConnections-0.0690.1470.0920.0930.239-0.0700.092-0.047-0.2070.515-0.1351.0000.0280.0500.0050.0250.0430.0670.0590.0180.0190.0000.0030.0020.0140.0100.0170.0240.0070.0110.0000.0040.0360.0340.0000.0080.0000.0000.0220.0130.0000.003
TownCount0.0450.2490.0210.0210.063-0.0800.021-0.3940.158-0.045-0.2440.0281.0000.0340.0600.0180.0240.0030.1090.5310.4160.0290.0290.1470.1460.3230.3210.0150.1280.7960.0090.0670.0690.0400.0180.3560.0120.0160.1270.0290.0030.018
No_Incidents0.0270.0200.6130.4490.0190.0150.9970.0480.0230.0000.0090.0500.0341.0000.0030.3240.5570.0000.0880.0490.0020.0000.0020.0180.0070.0100.0180.0380.0160.0890.0000.0080.0010.0700.0310.0470.0310.0560.0410.0000.0540.022
InspectionDay0.0380.0310.0050.0020.0320.0200.0020.0210.0240.0020.0430.0050.0600.0031.0000.0020.0040.0020.0260.1380.1640.0320.0590.0400.0490.0910.0300.0140.0570.0550.0350.0360.0330.0190.0000.0230.0000.0000.0340.0220.0000.016
Severity0.0230.0350.6160.4110.0460.0290.4150.0800.0200.0000.0050.0250.0180.3240.0021.0001.0000.0000.0890.0360.0070.0020.0000.0110.0060.0230.0110.0030.0090.0560.0000.0050.0030.0450.0600.0430.0690.1230.0350.0140.0950.020
Incidence0.0350.0600.7480.6270.0750.0480.7020.1320.0310.0000.0100.0430.0240.5570.0041.0001.0000.0000.0770.0530.0060.0020.0000.0110.0000.0020.0110.0020.0090.0500.0000.0050.0030.0380.0500.0300.0480.0950.0340.0140.0910.016
NumConnectionsUnder0.0120.0080.0000.0000.0000.0000.0000.0040.0030.0000.0000.0670.0030.0000.0020.0000.0001.0000.0040.0110.0060.0000.0000.0000.0020.0070.0060.0000.0000.0020.0000.0080.0030.0120.0000.0000.0000.0000.0050.0250.0000.000
gas_natural0.3270.3560.1550.1250.0470.0870.0930.2140.2850.0000.0580.0590.1090.0880.0260.0890.0770.0041.0000.2680.0430.0850.0090.0200.0040.0540.0810.0110.0290.1010.0330.0200.0410.2590.0000.0310.0000.0000.0220.0070.0000.165
Province0.2970.1650.0390.0240.1290.1350.0370.1550.1800.0140.1830.0180.5310.0490.1380.0360.0530.0110.2681.0001.0001.0000.1471.0001.0000.9950.9830.0900.9980.9950.0881.0000.1930.1200.0120.1930.0050.0020.1820.0830.0000.057
Autonomía_Andalucía0.2450.1170.0240.0180.1250.0970.0050.1370.0900.0090.0450.0190.4160.0020.1640.0070.0060.0060.0431.0001.0000.0160.0160.1100.0810.2770.1450.0080.0970.0940.0050.0370.0200.0280.0000.0340.0000.0000.0090.0550.0000.017
Autonomía_Aragón0.0300.0820.0050.0080.0380.0880.0030.0260.2040.0000.1470.0000.0290.0000.0320.0020.0020.0000.0851.0000.0161.0000.0010.0160.0120.0410.0210.0000.0140.0140.0000.0050.0750.0000.0000.0080.0000.0000.0600.0020.0000.000
Autonomía_Balears (Illes)0.0400.0670.0020.0160.0170.0440.0000.0260.0320.0000.0140.0030.0290.0020.0590.0000.0000.0000.0090.1470.0160.0011.0000.0160.0120.0400.0210.0000.0140.0140.0000.0050.0150.0030.0000.0080.0000.0000.0180.0030.0000.000
Autonomía_Castilla y León0.4510.1390.0190.0130.1190.0900.0180.0950.1650.0000.0710.0020.1470.0180.0400.0110.0110.0000.0201.0000.1100.0160.0161.0000.0810.2790.1460.0080.0980.0940.0050.0380.0050.0060.0000.0540.0000.0000.0270.0170.0000.006
Autonomía_Castilla-La Mancha0.2440.1350.0130.0110.0570.1030.0090.1050.0680.0000.1380.0140.1460.0070.0490.0060.0000.0020.0041.0000.0810.0120.0120.0811.0000.2040.1070.0060.0720.0690.0040.0280.0070.0190.0000.0400.0000.0000.0130.0180.0000.009
Autonomía_Cataluña0.4900.1910.0280.0140.1520.0670.0100.2540.0820.0020.1150.0100.3230.0100.0910.0230.0020.0070.0540.9950.2770.0410.0400.2790.2041.0000.3670.0210.2460.2370.0140.0940.0210.0510.0030.1180.0010.0050.0190.0100.0040.025
Autonomía_Comunidad Valenciana0.1780.1040.0190.0140.0800.0880.0180.1560.1650.0000.0470.0170.3210.0180.0300.0110.0110.0060.0810.9830.1450.0210.0210.1460.1070.3671.0000.0110.1290.1240.0070.0490.0260.0280.0000.0520.0000.0010.0610.0280.0010.013
Autonomía_Extremadura0.0430.0350.0750.0490.0790.0150.0410.0290.0350.0000.0070.0240.0150.0380.0140.0030.0020.0000.0110.0900.0080.0000.0000.0080.0060.0210.0111.0000.0070.0070.0000.0020.0080.1810.0000.0040.0000.0000.0300.0020.0000.000
Autonomía_Galicia0.3100.1310.0200.0140.0460.0680.0160.0990.0290.0000.1380.0070.1280.0160.0570.0090.0090.0000.0290.9980.0970.0140.0140.0980.0720.2460.1290.0071.0000.0830.0040.0330.0720.0180.0000.0480.0000.0000.0490.0110.0000.019
Autonomía_Madrid (Comunidad de)0.2720.2640.0960.0530.0770.1230.0890.0720.1540.0000.0590.0110.7960.0890.0550.0560.0500.0020.1010.9950.0940.0140.0140.0940.0690.2370.1240.0070.0831.0000.0040.0320.0150.0610.0140.0490.0080.0000.0480.0100.0000.024
Autonomía_Navarra (Comunidad Foral de)0.0320.0340.0000.0030.0340.0340.0000.0040.0110.0000.0360.0000.0090.0000.0350.0000.0000.0000.0330.0880.0050.0000.0000.0050.0040.0140.0070.0000.0040.0041.0000.0000.0260.0040.0000.0020.0000.0000.0220.0000.0000.000
Autonomía_Rioja (La)0.1180.0600.0070.0050.0300.0450.0070.0430.0690.0000.0520.0040.0670.0080.0360.0050.0050.0080.0201.0000.0370.0050.0050.0380.0280.0940.0490.0020.0330.0320.0001.0000.0040.0080.0000.0180.0000.0000.0140.0020.0000.004
Material_Acrylonitrile-Butadiene-Styrene0.1190.1380.0090.0130.1470.3680.0070.1090.3540.0100.8760.0360.0690.0010.0330.0030.0030.0030.0410.1930.0200.0750.0150.0050.0070.0210.0260.0080.0720.0150.0260.0041.0000.0260.0000.0550.0000.0000.8140.0350.0000.012
Material_Copper0.1320.1210.1040.0810.0400.0420.0710.0840.1340.0000.0230.0340.0400.0700.0190.0450.0380.0120.2590.1200.0280.0000.0030.0060.0190.0510.0280.1810.0180.0610.0040.0080.0261.0000.0000.0130.0000.0000.1880.0080.0000.002
Material_Fiberglass0.0050.0060.0820.0560.0480.0040.0680.0350.0040.0000.0000.0000.0180.0310.0000.0600.0500.0000.0000.0120.0000.0000.0000.0000.0000.0030.0000.0000.0000.0140.0000.0000.0000.0001.0000.0000.0000.0000.0090.0000.0000.000
Material_Fiberglass-Reinforced Plastic0.1580.0720.0590.0350.0320.0250.0450.4270.2260.0000.0490.0080.3560.0470.0230.0430.0300.0000.0310.1930.0340.0080.0080.0540.0400.1180.0520.0040.0480.0490.0020.0180.0550.0130.0001.0000.0000.0000.3940.0170.0000.006
Material_Flexible Polyolefin0.0040.0050.0740.1230.0240.0000.0610.0140.0030.0000.0000.0000.0120.0310.0000.0690.0480.0000.0000.0050.0000.0000.0000.0000.0000.0010.0000.0000.0000.0080.0000.0000.0000.0000.0000.0001.0000.0000.0050.0000.0000.000
Material_Polyamide0.0060.0110.1820.1200.0170.0000.1360.0420.0020.0000.0000.0000.0160.0560.0000.1230.0950.0000.0000.0020.0000.0000.0000.0000.0000.0050.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0110.0000.0000.000
Material_Polyethylene0.1240.1640.0460.0390.1130.3060.0430.2560.3710.0080.7130.0220.1270.0410.0340.0350.0340.0050.0220.1820.0090.0600.0180.0270.0130.0190.0610.0300.0490.0480.0220.0140.8140.1880.0090.3940.0050.0111.0000.2530.0110.087
Material_Polypropylene0.3010.2230.0250.0170.0330.2000.0240.1310.0550.0000.0310.0130.0290.0000.0220.0140.0140.0250.0070.0830.0550.0020.0030.0170.0180.0100.0280.0020.0110.0100.0000.0020.0350.0080.0000.0170.0000.0000.2531.0000.0000.003
Material_Polyvinylidene Fluoride0.0020.0100.1340.1600.0110.0000.1300.0260.0030.0000.0000.0000.0030.0540.0000.0950.0910.0000.0000.0000.0000.0000.0000.0000.0000.0040.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0001.0000.000
Material_Zinc-Coated Steel0.0610.0780.0500.0450.0140.0130.0240.0370.0460.0000.0100.0030.0180.0220.0160.0200.0160.0000.1650.0570.0170.0000.0000.0060.0090.0250.0130.0000.0190.0240.0000.0040.0120.0020.0000.0060.0000.0000.0870.0030.0001.000

Missing values

2023-02-13T02:53:00.411392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T02:53:01.968907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PipeIdInspectionsNo_IncidentsRisk_S*I/Inspectionsleakage_estimate_factorInspectionDayInspectionYearInspectionDateMonthsLastRevRisk_S*ISeverityIncidenceYearBuiltDiameterLengthPressureNumConnectionsNumConnectionsUnderBoolBridlegas_naturalProvinceTownTownCountAutonomía_AndalucíaAutonomía_AragónAutonomía_Balears (Illes)Autonomía_Castilla y LeónAutonomía_Castilla-La ManchaAutonomía_CataluñaAutonomía_Comunidad ValencianaAutonomía_ExtremaduraAutonomía_GaliciaAutonomía_Madrid (Comunidad de)Autonomía_Navarra (Comunidad Foral de)Autonomía_Rioja (La)Material_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_FiberglassMaterial_Fiberglass-Reinforced PlasticMaterial_Flexible PolyolefinMaterial_Flexible Polyvinyl ChlorideMaterial_PolyamideMaterial_PolyethyleneMaterial_PolypropyleneMaterial_Polyvinylidene FluorideMaterial_Zinc-Coated Steel
056922465100.00.0Thursday20202020-12-31240.040199363.01.7784.0000001valenciabetera225000000010000000000001000
1188341482600.00.0Thursday20212020-12-31230.0401995200.034.9600.0250001barcelonasabadell1588700000100000000000001000
2189485681600.00.0Thursday20202020-12-31230.040195050.816.4234.0000001valenciabetera225000000010000010000000000
3189485654600.00.0Thursday20202020-12-31230.040195050.811.4434.0000001valenciabetera225000000010000010000000000
4274990283600.00.0Thursday20212020-12-31230.0402005160.010.3770.0250001barcelonasabadell1588700000100000000000001000
5274925411600.00.0Thursday20212020-12-31230.0402005200.013.4970.0251001barcelonasabadell1588700000100000000000001000
6189538742600.00.0Thursday20202020-12-31230.040195050.852.9574.0000001valenciabetera225000000010000010000000000
7274990929600.00.0Thursday20212020-12-31230.0401995200.03.4700.0250001barcelonasabadell1588700000100000000000001000
8188341464600.00.0Thursday20212020-12-31230.0401995200.01.3730.0250001barcelonasabadell1588700000100000000000001000
9189215318600.00.0Thursday20212020-12-31230.0402000250.08.9300.0250001barcelonasabadell1588700000100000000000001000
PipeIdInspectionsNo_IncidentsRisk_S*I/Inspectionsleakage_estimate_factorInspectionDayInspectionYearInspectionDateMonthsLastRevRisk_S*ISeverityIncidenceYearBuiltDiameterLengthPressureNumConnectionsNumConnectionsUnderBoolBridlegas_naturalProvinceTownTownCountAutonomía_AndalucíaAutonomía_AragónAutonomía_Balears (Illes)Autonomía_Castilla y LeónAutonomía_Castilla-La ManchaAutonomía_CataluñaAutonomía_Comunidad ValencianaAutonomía_ExtremaduraAutonomía_GaliciaAutonomía_Madrid (Comunidad de)Autonomía_Navarra (Comunidad Foral de)Autonomía_Rioja (La)Material_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_FiberglassMaterial_Fiberglass-Reinforced PlasticMaterial_Flexible PolyolefinMaterial_Flexible Polyvinyl ChlorideMaterial_PolyamideMaterial_PolyethyleneMaterial_PolypropyleneMaterial_Polyvinylidene FluorideMaterial_Zinc-Coated Steel
1436382333980653100.00.0Friday20102010-10-08240.0402008160.00.6000.150001tarragonacalafell368700000100000000000001000
1436383333980637100.00.0Friday20102010-10-08240.0402008160.00.6010.150001tarragonacalafell368700000100000000000001000
1436384190852729100.00.0Friday20102010-10-08240.040200490.00.5010.150001tarragonacalafell368700000100000000000001000
1436385331062012100.00.0Wednesday20102010-10-06240.040200890.01.0000.150001tarragonacalafell368700000100000000000001000
1436386333980664100.00.0Wednesday20102010-10-06240.0402008160.00.6010.150001tarragonacalafell368700000100000000000001000
1436387189142507100.00.0Wednesday20102010-10-06240.0402000110.00.6940.150001tarragonaamposta188900000100000000000001000
1436388189141476100.00.0Tuesday20102010-10-05240.0402000110.01.1880.150001tarragonacalafell368700000100000000000001000
1436389324551020100.00.0Tuesday20102010-10-05240.0402008110.00.8020.100001barcelonasentmenat131100000100000000000001000
1436390190908195100.00.0Tuesday20102010-10-05240.0402004200.00.9990.150001alicantealicante/alacant1389900000010000000000001000
1436391340613298100.00.0Friday20102010-10-01210.040200990.01.1010.150001tarragonacalafell368700000100000000000001000